ERI: An Edge-Based Staleness-Resilient Data Synthesis Framework for Edge AI application in — NSF Award to University of North Dako
As digital infrastructure grows increasingly dependent on real-time data from distributed sensors and devices, the need to monitor network traffic locally and reliably becomes crucial. This project addresses a critical challenge in edge computing, i.e., how to provide accurate and up-to-date data to Edge AI application
| Award title | ERI: An Edge-Based Staleness-Resilient Data Synthesis Framework for Edge AI application in |
|---|---|
| Award ID | 2502132 |
| Awardee | University of North Dakota Main Campus |
| City | GRAND FORKS |
| State | ND |
| Amount obligated | $199,993 |
| Principal investigator | Jielun Zhang |
| Program | ERI-Eng. Research Initiation |
| Start date | 10/01/2025 |
| Abstract | As digital infrastructure grows increasingly dependent on real-time data from distributed sensors and devices, the need to monitor network traffic locally and reliably becomes crucial. This project addresses a critical challenge in edge computing, i.e., how to provide accurate and up-to-date data to Edge AI applications while preserving user privacy and minimizing communication overhead. Traditional methods struggle with outdated data, privacy limitations, and limited processing power at the edg |
| Source | NSF Awards |
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